Heart FACS Notebook
Specify the tissue of interest, run the boilerplate code which sets up the functions and environment, load the tissue object.
tissue_of_interest = "Heart"
library(here)
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
load_tissue_facs(tissue_of_interest)Performing log-normalization
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|================================================================================| 100%Calculating gene means
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TEXT_SHOW_BACKTRACE environmental variable.An object of class seurat in project Heart
23341 genes across 4365 samples.Visualize top genes in principal components
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 10The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.5
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
Check expression of genes of interset.
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7 8 9 10 11
993 894 620 545 398 326 221 182 143 131 80 52 Which markers identify a specific cluster?
clust.markers <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06m 04sprint(x = head(x= clust.markers, n = 10))You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|++++++++++++++++++++ | 39% ~52s
|+++++++++++++++++++++ | 40% ~51s
|+++++++++++++++++++++ | 41% ~50s
|++++++++++++++++++++++ | 42% ~49s
|++++++++++++++++++++++ | 43% ~48s
|+++++++++++++++++++++++ | 44% ~47s
|+++++++++++++++++++++++ | 45% ~46s
|++++++++++++++++++++++++ | 46% ~45s
|++++++++++++++++++++++++ | 47% ~45s
|+++++++++++++++++++++++++ | 48% ~44s
|+++++++++++++++++++++++++ | 49% ~43s
|++++++++++++++++++++++++++ | 51% ~42s
|++++++++++++++++++++++++++ | 52% ~41s
|+++++++++++++++++++++++++++ | 53% ~40s
|+++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 55% ~39s
|++++++++++++++++++++++++++++ | 56% ~38s
|+++++++++++++++++++++++++++++ | 57% ~37s
|+++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~33s
|+++++++++++++++++++++++++++++++ | 62% ~32s
|++++++++++++++++++++++++++++++++ | 63% ~32s
|++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|+++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|++++++++++++++++++++++++++++++++++++ | 70% ~25s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|+++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s
| | 0 % ~calculating
|+ | 1 % ~59s
|++ | 2 % ~59s
|++ | 3 % ~57s
|+++ | 4 % ~55s
|+++ | 5 % ~54s
|++++ | 6 % ~54s
|++++ | 7 % ~54s
|+++++ | 9 % ~54s
|+++++ | 10% ~53s
|++++++ | 11% ~53s
|++++++ | 12% ~52s
|+++++++ | 13% ~52s
|+++++++ | 14% ~51s
|++++++++ | 15% ~50s
|++++++++ | 16% ~49s
|+++++++++ | 17% ~49s
|++++++++++ | 18% ~48s
|++++++++++ | 19% ~48s
|+++++++++++ | 20% ~47s
|+++++++++++ | 21% ~46s
|++++++++++++ | 22% ~46s
|++++++++++++ | 23% ~45s
|+++++++++++++ | 24% ~44s
|+++++++++++++ | 26% ~44s
|++++++++++++++ | 27% ~43s
|++++++++++++++ | 28% ~42s
|+++++++++++++++ | 29% ~42s
|+++++++++++++++ | 30% ~41s
|++++++++++++++++ | 31% ~40s
|++++++++++++++++ | 32% ~40s
|+++++++++++++++++ | 33% ~39s
|++++++++++++++++++ | 34% ~39s
|++++++++++++++++++ | 35% ~38s
|+++++++++++++++++++ | 36% ~37s
|+++++++++++++++++++ | 37% ~37s
|++++++++++++++++++++ | 38% ~36s
|++++++++++++++++++++ | 39% ~35s
|+++++++++++++++++++++ | 40% ~35s
|+++++++++++++++++++++ | 41% ~34s
|++++++++++++++++++++++ | 43% ~33s
|++++++++++++++++++++++ | 44% ~33s
|+++++++++++++++++++++++ | 45% ~32s
|+++++++++++++++++++++++ | 46% ~32s
|++++++++++++++++++++++++ | 47% ~31s
|++++++++++++++++++++++++ | 48% ~30s
|+++++++++++++++++++++++++ | 49% ~30s
|+++++++++++++++++++++++++ | 50% ~29s
|++++++++++++++++++++++++++ | 51% ~29s
|+++++++++++++++++++++++++++ | 52% ~28s
|+++++++++++++++++++++++++++ | 53% ~27s
|++++++++++++++++++++++++++++ | 54% ~27s
|++++++++++++++++++++++++++++ | 55% ~26s
|+++++++++++++++++++++++++++++ | 56% ~25s
|+++++++++++++++++++++++++++++ | 57% ~25s
|++++++++++++++++++++++++++++++ | 59% ~24s
|++++++++++++++++++++++++++++++ | 60% ~24s
|+++++++++++++++++++++++++++++++ | 61% ~23s
|+++++++++++++++++++++++++++++++ | 62% ~22s
|++++++++++++++++++++++++++++++++ | 63% ~22s
|++++++++++++++++++++++++++++++++ | 64% ~21s
|+++++++++++++++++++++++++++++++++ | 65% ~20s
|+++++++++++++++++++++++++++++++++ | 66% ~20s
|++++++++++++++++++++++++++++++++++ | 67% ~19s
|+++++++++++++++++++++++++++++++++++ | 68% ~19s
|+++++++++++++++++++++++++++++++++++ | 69% ~18s
|++++++++++++++++++++++++++++++++++++ | 70% ~17s
|++++++++++++++++++++++++++++++++++++ | 71% ~17s
|+++++++++++++++++++++++++++++++++++++ | 72% ~16s
|+++++++++++++++++++++++++++++++++++++ | 73% ~16s
|++++++++++++++++++++++++++++++++++++++ | 74% ~15s
|++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~12s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 58s
| | 0 % ~calculating
|+ | 1 % ~01m 04s
|++ | 2 % ~01m 04s
|++ | 3 % ~01m 04s
|+++ | 4 % ~01m 02s
|+++ | 5 % ~01m 01s
|++++ | 6 % ~60s
|++++ | 7 % ~60s
|+++++ | 8 % ~59s
|+++++ | 9 % ~59s
|++++++ | 10% ~58s
|++++++ | 11% ~57s
|+++++++ | 12% ~57s
|+++++++ | 13% ~56s
|++++++++ | 14% ~55s
|++++++++ | 15% ~54s
|+++++++++ | 16% ~54s
|+++++++++ | 18% ~53s
|++++++++++ | 19% ~53s
|++++++++++ | 20% ~52s
|+++++++++++ | 21% ~52s
|+++++++++++ | 22% ~51s
|++++++++++++ | 23% ~50s
|++++++++++++ | 24% ~50s
|+++++++++++++ | 25% ~49s
|+++++++++++++ | 26% ~48s
|++++++++++++++ | 27% ~48s
|++++++++++++++ | 28% ~47s
|+++++++++++++++ | 29% ~46s
|+++++++++++++++ | 30% ~46s
|++++++++++++++++ | 31% ~45s
|++++++++++++++++ | 32% ~44s
|+++++++++++++++++ | 33% ~44s
|++++++++++++++++++ | 34% ~43s
|++++++++++++++++++ | 35% ~42s
|+++++++++++++++++++ | 36% ~42s
|+++++++++++++++++++ | 37% ~41s
|++++++++++++++++++++ | 38% ~40s
|++++++++++++++++++++ | 39% ~40s
|+++++++++++++++++++++ | 40% ~39s
|+++++++++++++++++++++ | 41% ~38s
|++++++++++++++++++++++ | 42% ~38s
|++++++++++++++++++++++ | 43% ~37s
|+++++++++++++++++++++++ | 44% ~36s
|+++++++++++++++++++++++ | 45% ~36s
|++++++++++++++++++++++++ | 46% ~35s
|++++++++++++++++++++++++ | 47% ~34s
|+++++++++++++++++++++++++ | 48% ~34s
|+++++++++++++++++++++++++ | 49% ~33s
|++++++++++++++++++++++++++ | 51% ~32s
|++++++++++++++++++++++++++ | 52% ~32s
|+++++++++++++++++++++++++++ | 53% ~31s
|+++++++++++++++++++++++++++ | 54% ~30s
|++++++++++++++++++++++++++++ | 55% ~30s
|++++++++++++++++++++++++++++ | 56% ~29s
|+++++++++++++++++++++++++++++ | 57% ~28s
|+++++++++++++++++++++++++++++ | 58% ~28s
|++++++++++++++++++++++++++++++ | 59% ~27s
|++++++++++++++++++++++++++++++ | 60% ~26s
|+++++++++++++++++++++++++++++++ | 61% ~26s
|+++++++++++++++++++++++++++++++ | 62% ~25s
|++++++++++++++++++++++++++++++++ | 63% ~24s
|++++++++++++++++++++++++++++++++ | 64% ~24s
|+++++++++++++++++++++++++++++++++ | 65% ~23s
|+++++++++++++++++++++++++++++++++ | 66% ~22s
|++++++++++++++++++++++++++++++++++ | 67% ~22s
|+++++++++++++++++++++++++++++++++++ | 68% ~21s
|+++++++++++++++++++++++++++++++++++ | 69% ~20s
|++++++++++++++++++++++++++++++++++++ | 70% ~20s
|++++++++++++++++++++++++++++++++++++ | 71% ~19s
|+++++++++++++++++++++++++++++++++++++ | 72% ~18s
|+++++++++++++++++++++++++++++++++++++ | 73% ~18s
|++++++++++++++++++++++++++++++++++++++ | 74% ~17s
|++++++++++++++++++++++++++++++++++++++ | 75% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~15s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 05s
| | 0 % ~calculating
|+ | 1 % ~48s
|++ | 2 % ~48s
|++ | 3 % ~48s
|+++ | 4 % ~48s
|+++ | 5 % ~48s
|++++ | 6 % ~47s
|++++ | 7 % ~46s
|+++++ | 8 % ~46s
|+++++ | 9 % ~46s
|++++++ | 10% ~46s
|++++++ | 11% ~45s
|+++++++ | 12% ~44s
|+++++++ | 13% ~44s
|++++++++ | 14% ~43s
|++++++++ | 15% ~43s
|+++++++++ | 16% ~42s
|+++++++++ | 17% ~42s
|++++++++++ | 18% ~41s
|++++++++++ | 19% ~41s
|+++++++++++ | 20% ~40s
|+++++++++++ | 21% ~40s
|++++++++++++ | 22% ~39s
|++++++++++++ | 23% ~39s
|+++++++++++++ | 24% ~38s
|+++++++++++++ | 26% ~38s
|++++++++++++++ | 27% ~37s
|++++++++++++++ | 28% ~37s
|+++++++++++++++ | 29% ~36s
|+++++++++++++++ | 30% ~36s
|++++++++++++++++ | 31% ~35s
|++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
|+++++++++++++++++ | 34% ~34s
|++++++++++++++++++ | 35% ~33s
|++++++++++++++++++ | 36% ~33s
|+++++++++++++++++++ | 37% ~32s
|+++++++++++++++++++ | 38% ~32s
|++++++++++++++++++++ | 39% ~31s
|++++++++++++++++++++ | 40% ~31s
|+++++++++++++++++++++ | 41% ~30s
|+++++++++++++++++++++ | 42% ~30s
|++++++++++++++++++++++ | 43% ~29s
|++++++++++++++++++++++ | 44% ~28s
|+++++++++++++++++++++++ | 45% ~28s
|+++++++++++++++++++++++ | 46% ~27s
|++++++++++++++++++++++++ | 47% ~27s
|++++++++++++++++++++++++ | 48% ~26s
|+++++++++++++++++++++++++ | 49% ~26s
|+++++++++++++++++++++++++ | 50% ~25s
|++++++++++++++++++++++++++ | 51% ~25s
|+++++++++++++++++++++++++++ | 52% ~24s
|+++++++++++++++++++++++++++ | 53% ~24s
|++++++++++++++++++++++++++++ | 54% ~23s
|++++++++++++++++++++++++++++ | 55% ~23s
|+++++++++++++++++++++++++++++ | 56% ~22s
|+++++++++++++++++++++++++++++ | 57% ~22s
|++++++++++++++++++++++++++++++ | 58% ~21s
|++++++++++++++++++++++++++++++ | 59% ~21s
|+++++++++++++++++++++++++++++++ | 60% ~20s
|+++++++++++++++++++++++++++++++ | 61% ~20s
|++++++++++++++++++++++++++++++++ | 62% ~19s
|++++++++++++++++++++++++++++++++ | 63% ~19s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 65% ~18s
|++++++++++++++++++++++++++++++++++ | 66% ~17s
|++++++++++++++++++++++++++++++++++ | 67% ~17s
|+++++++++++++++++++++++++++++++++++ | 68% ~16s
|+++++++++++++++++++++++++++++++++++ | 69% ~16s
|++++++++++++++++++++++++++++++++++++ | 70% ~15s
|++++++++++++++++++++++++++++++++++++ | 71% ~15s
|+++++++++++++++++++++++++++++++++++++ | 72% ~14s
|+++++++++++++++++++++++++++++++++++++ | 73% ~13s
|++++++++++++++++++++++++++++++++++++++ | 74% ~13s
|++++++++++++++++++++++++++++++++++++++ | 76% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 51s
| | 0 % ~calculating
|+ | 1 % ~01m 50s
|++ | 2 % ~01m 53s
|++ | 3 % ~01m 51s
|+++ | 4 % ~01m 50s
|+++ | 5 % ~01m 49s
|++++ | 6 % ~01m 48s
|++++ | 7 % ~01m 47s
|+++++ | 8 % ~01m 46s
|+++++ | 9 % ~01m 44s
|++++++ | 10% ~01m 44s
|++++++ | 11% ~01m 43s
|+++++++ | 12% ~01m 42s
|+++++++ | 13% ~01m 41s
|++++++++ | 14% ~01m 39s
|++++++++ | 15% ~01m 38s
|+++++++++ | 16% ~01m 37s
|+++++++++ | 17% ~01m 36s
|++++++++++ | 18% ~01m 34s
|++++++++++ | 19% ~01m 33s
|+++++++++++ | 20% ~01m 32s
|+++++++++++ | 21% ~01m 31s
|++++++++++++ | 22% ~01m 29s
|++++++++++++ | 23% ~01m 28s
|+++++++++++++ | 24% ~01m 27s
|+++++++++++++ | 26% ~01m 26s
|++++++++++++++ | 27% ~01m 25s
|++++++++++++++ | 28% ~01m 24s
|+++++++++++++++ | 29% ~01m 23s
|+++++++++++++++ | 30% ~01m 21s
|++++++++++++++++ | 31% ~01m 20s
|++++++++++++++++ | 32% ~01m 19s
|+++++++++++++++++ | 33% ~01m 18s
|+++++++++++++++++ | 34% ~01m 16s
|++++++++++++++++++ | 35% ~01m 15s
|++++++++++++++++++ | 36% ~01m 14s
|+++++++++++++++++++ | 37% ~01m 13s
|+++++++++++++++++++ | 38% ~01m 12s
|++++++++++++++++++++ | 39% ~01m 11s
|++++++++++++++++++++ | 40% ~01m 09s
|+++++++++++++++++++++ | 41% ~01m 08s
|+++++++++++++++++++++ | 42% ~01m 07s
|++++++++++++++++++++++ | 43% ~01m 06s
|++++++++++++++++++++++ | 44% ~01m 05s
|+++++++++++++++++++++++ | 45% ~01m 04s
|+++++++++++++++++++++++ | 46% ~01m 02s
|++++++++++++++++++++++++ | 47% ~01m 01s
|++++++++++++++++++++++++ | 48% ~01m 00s
|+++++++++++++++++++++++++ | 49% ~59s
|+++++++++++++++++++++++++ | 50% ~58s
|++++++++++++++++++++++++++ | 51% ~57s
|+++++++++++++++++++++++++++ | 52% ~55s
|+++++++++++++++++++++++++++ | 53% ~54s
|++++++++++++++++++++++++++++ | 54% ~53s
|++++++++++++++++++++++++++++ | 55% ~52s
|+++++++++++++++++++++++++++++ | 56% ~51s
|+++++++++++++++++++++++++++++ | 57% ~50s
|++++++++++++++++++++++++++++++ | 58% ~49s
|++++++++++++++++++++++++++++++ | 59% ~48s
|+++++++++++++++++++++++++++++++ | 60% ~47s
|+++++++++++++++++++++++++++++++ | 61% ~46s
|++++++++++++++++++++++++++++++++ | 62% ~45s
|++++++++++++++++++++++++++++++++ | 63% ~43s
|+++++++++++++++++++++++++++++++++ | 64% ~42s
|+++++++++++++++++++++++++++++++++ | 65% ~41s
|++++++++++++++++++++++++++++++++++ | 66% ~40s
|++++++++++++++++++++++++++++++++++ | 67% ~39s
|+++++++++++++++++++++++++++++++++++ | 68% ~37s
|+++++++++++++++++++++++++++++++++++ | 69% ~36s
|++++++++++++++++++++++++++++++++++++ | 70% ~35s
|++++++++++++++++++++++++++++++++++++ | 71% ~34s
|+++++++++++++++++++++++++++++++++++++ | 72% ~33s
|+++++++++++++++++++++++++++++++++++++ | 73% ~31s
|++++++++++++++++++++++++++++++++++++++ | 74% ~30s
|++++++++++++++++++++++++++++++++++++++ | 76% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~28s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~25s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~23s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~19s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 58s
| | 0 % ~calculating
|+ | 1 % ~01m 33s
|++ | 2 % ~01m 35s
|++ | 3 % ~01m 33s
|+++ | 4 % ~01m 31s
|+++ | 5 % ~01m 30s
|++++ | 6 % ~01m 30s
|++++ | 7 % ~01m 28s
|+++++ | 8 % ~01m 28s
|+++++ | 9 % ~01m 27s
|++++++ | 10% ~01m 26s
|++++++ | 11% ~01m 26s
|+++++++ | 12% ~01m 25s
|+++++++ | 13% ~01m 24s
|++++++++ | 14% ~01m 23s
|++++++++ | 15% ~01m 22s
|+++++++++ | 16% ~01m 21s
|+++++++++ | 18% ~01m 20s
|++++++++++ | 19% ~01m 19s
|++++++++++ | 20% ~01m 18s
|+++++++++++ | 21% ~01m 17s
|+++++++++++ | 22% ~01m 16s
|++++++++++++ | 23% ~01m 15s
|++++++++++++ | 24% ~01m 13s
|+++++++++++++ | 25% ~01m 13s
|+++++++++++++ | 26% ~01m 12s
|++++++++++++++ | 27% ~01m 10s
|++++++++++++++ | 28% ~01m 10s
|+++++++++++++++ | 29% ~01m 09s
|+++++++++++++++ | 30% ~01m 08s
|++++++++++++++++ | 31% ~01m 07s
|++++++++++++++++ | 32% ~01m 06s
|+++++++++++++++++ | 33% ~01m 05s
|++++++++++++++++++ | 34% ~01m 04s
|++++++++++++++++++ | 35% ~01m 03s
|+++++++++++++++++++ | 36% ~01m 02s
|+++++++++++++++++++ | 37% ~01m 01s
|++++++++++++++++++++ | 38% ~60s
|++++++++++++++++++++ | 39% ~59s
|+++++++++++++++++++++ | 40% ~58s
|+++++++++++++++++++++ | 41% ~57s
|++++++++++++++++++++++ | 42% ~56s
|++++++++++++++++++++++ | 43% ~55s
|+++++++++++++++++++++++ | 44% ~54s
|+++++++++++++++++++++++ | 45% ~53s
|++++++++++++++++++++++++ | 46% ~52s
|++++++++++++++++++++++++ | 47% ~51s
|+++++++++++++++++++++++++ | 48% ~50s
|+++++++++++++++++++++++++ | 49% ~49s
|++++++++++++++++++++++++++ | 51% ~48s
|++++++++++++++++++++++++++ | 52% ~47s
|+++++++++++++++++++++++++++ | 53% ~46s
|+++++++++++++++++++++++++++ | 54% ~45s
|++++++++++++++++++++++++++++ | 55% ~44s
|++++++++++++++++++++++++++++ | 56% ~43s
|+++++++++++++++++++++++++++++ | 57% ~42s
|+++++++++++++++++++++++++++++ | 58% ~41s
|++++++++++++++++++++++++++++++ | 59% ~40s
|++++++++++++++++++++++++++++++ | 60% ~39s
|+++++++++++++++++++++++++++++++ | 61% ~38s
|+++++++++++++++++++++++++++++++ | 62% ~37s
|++++++++++++++++++++++++++++++++ | 63% ~36s
|++++++++++++++++++++++++++++++++ | 64% ~35s
|+++++++++++++++++++++++++++++++++ | 65% ~34s
|+++++++++++++++++++++++++++++++++ | 66% ~33s
|++++++++++++++++++++++++++++++++++ | 67% ~32s
|+++++++++++++++++++++++++++++++++++ | 68% ~31s
|+++++++++++++++++++++++++++++++++++ | 69% ~30s
|++++++++++++++++++++++++++++++++++++ | 70% ~29s
|++++++++++++++++++++++++++++++++++++ | 71% ~28s
|+++++++++++++++++++++++++++++++++++++ | 72% ~27s
|+++++++++++++++++++++++++++++++++++++ | 73% ~26s
|++++++++++++++++++++++++++++++++++++++ | 74% ~25s
|++++++++++++++++++++++++++++++++++++++ | 75% ~24s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~23s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~22s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~21s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~20s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~19s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~17s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~13s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 36s
| | 0 % ~calculating
|+ | 1 % ~01m 14s
|++ | 2 % ~01m 15s
|++ | 3 % ~01m 13s
|+++ | 4 % ~01m 11s
|+++ | 5 % ~01m 11s
|++++ | 6 % ~01m 10s
|++++ | 7 % ~01m 09s
|+++++ | 8 % ~01m 08s
|+++++ | 9 % ~01m 07s
|++++++ | 11% ~01m 07s
|++++++ | 12% ~01m 06s
|+++++++ | 13% ~01m 05s
|+++++++ | 14% ~01m 04s
|++++++++ | 15% ~01m 04s
|++++++++ | 16% ~01m 03s
|+++++++++ | 17% ~01m 02s
|+++++++++ | 18% ~01m 01s
|++++++++++ | 19% ~01m 01s
|++++++++++ | 20% ~60s
|+++++++++++ | 21% ~59s
|++++++++++++ | 22% ~58s
|++++++++++++ | 23% ~58s
|+++++++++++++ | 24% ~57s
|+++++++++++++ | 25% ~56s
|++++++++++++++ | 26% ~55s
|++++++++++++++ | 27% ~54s
|+++++++++++++++ | 28% ~53s
|+++++++++++++++ | 29% ~53s
|++++++++++++++++ | 31% ~52s
|++++++++++++++++ | 32% ~51s
|+++++++++++++++++ | 33% ~50s
|+++++++++++++++++ | 34% ~49s
|++++++++++++++++++ | 35% ~49s
|++++++++++++++++++ | 36% ~48s
|+++++++++++++++++++ | 37% ~47s
|+++++++++++++++++++ | 38% ~46s
|++++++++++++++++++++ | 39% ~46s
|++++++++++++++++++++ | 40% ~45s
|+++++++++++++++++++++ | 41% ~44s
|++++++++++++++++++++++ | 42% ~43s
|++++++++++++++++++++++ | 43% ~43s
|+++++++++++++++++++++++ | 44% ~42s
|+++++++++++++++++++++++ | 45% ~41s
|++++++++++++++++++++++++ | 46% ~40s
|++++++++++++++++++++++++ | 47% ~39s
|+++++++++++++++++++++++++ | 48% ~39s
|+++++++++++++++++++++++++ | 49% ~38s
|++++++++++++++++++++++++++ | 51% ~37s
|++++++++++++++++++++++++++ | 52% ~36s
|+++++++++++++++++++++++++++ | 53% ~35s
|+++++++++++++++++++++++++++ | 54% ~35s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 53sDisplay the top markers you computed above.
#tiss.markers %>% group_by(cluster) %>% top_n(5, avg_diff)Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11)
cell_ontology_class <-
c(
"fibroblast",
"endothelial cell",
"fibroblast",
"fibroblast",
"leukocyte",
"endothelial cell",
"smooth muscle cell",
"endocardial cell",
"cardiac muscle cell",
"leukocyte",
"endothelial cell",
"erythrocyte"
)
cell_ontology_id <-
c(
"CL:0000057",
"CL:0000115",
"CL:0000057",
"CL:0000057",
"CL:0000738",
"CL:0000115",
"CL:0000192",
"CL:0002350",
"CL:0000746",
"CL:0000738",
"CL:0000115",
"CL:0000232"
)
tiss@meta.data[,'free_annotation'] <- NA
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
Fb_3 all seem to be derived from mouse_38_F.
TSNEPlot(object = tiss, do.return = TRUE, group.by = “mouse.id”)
Edc all are derived from aorta.
TSNEPlot(object = tiss, do.return = TRUE, group.by = “subtissue”)
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
B000412 B000633 B000634 B000636 B002010 B002011 B002421 B002423 B002427 B002428
0 14 20 81 5 12 4 19 33 147 18
1 58 45 24 42 39 5 77 51 42 4
10 0 0 0 0 0 0 0 0 0 0
11 1 1 0 3 0 0 0 0 0 0
2 4 113 39 1 186 30 2 78 35 19
3 2 4 0 1 8 155 2 16 51 227
4 4 22 5 4 28 9 9 25 20 9
5 35 3 2 23 1 2 37 32 7 11
6 6 10 4 2 5 2 9 9 9 6
7 1 27 22 0 27 10 4 3 14 18
8 0 4 1 1 2 1 2 0 3 1
9 3 4 0 3 6 2 5 13 3 2
B002429 B002430 B002431 MAA000398 MAA000399 MAA000400 MAA000452 MAA000586 MAA000587
0 4 0 0 76 26 88 47 44 23
1 12 1 14 37 58 15 81 23 28
10 0 0 0 0 0 0 0 0 0
11 0 2 7 0 0 0 0 0 1
2 1 0 0 5 1 11 2 56 0
3 6 3 15 0 0 0 0 8 1
4 2 0 0 20 8 13 16 36 13
5 78 7 17 0 11 0 3 1 2
6 7 0 0 17 13 11 14 8 12
7 0 4 0 3 2 4 0 8 1
8 0 0 0 2 0 1 0 2 0
9 4 2 2 2 1 2 1 10 2
MAA000589 MAA000594 MAA000595 MAA000898 MAA000899 MAA000901 MAA000903 MAA000906
0 25 20 30 5 15 16 22 1
1 31 0 0 12 1 33 41 29
10 0 0 0 0 0 0 0 1
11 0 19 0 0 0 0 1 10
2 3 0 0 0 13 1 9 0
3 0 19 13 1 0 0 5 2
4 13 2 5 3 7 16 28 0
5 9 3 0 3 0 5 4 6
6 11 2 0 0 1 6 14 1
7 2 0 0 0 4 2 4 1
8 0 1 2 0 1 0 2 0
9 1 20 10 0 1 2 7 0
MAA000908 MAA000917 MAA000918 MAA000919 MAA000920 MAA000936 MAA100037 MAA100096
0 2 4 21 52 80 38 1 0
1 6 7 16 22 21 17 2 0
10 79 0 0 0 0 0 0 0
11 3 2 0 0 2 0 0 0
2 0 1 0 0 0 9 0 0
3 1 2 0 1 1 0 1 0
4 0 6 17 12 3 43 0 0
5 0 18 0 0 2 0 4 0
6 0 3 8 6 8 16 1 0
7 2 0 1 8 6 3 1 0
8 0 0 0 8 8 0 41 28
9 0 3 1 2 0 17 0 0
MAA100097
0 0
1 0
10 0
11 0
2 1
3 0
4 0
5 0
6 0
7 0
8 32
9 0Subset and iterate
We can repeat the above analysis on a subset of cells, defined using cluster IDs or some other metadata. This is a good way to drill down and find substructure.
First subset
# Subset data based on cluster id
subtiss <- SubsetData(object = tiss, ident.use = c(6), do.center = F, do.scale = F, cells.use = )
# To subset data based on cell_ontology_class or other metadata, you can explicitly pass cell names
# anno = 'exocrine cells'
# cells.to.use = tiss@cell.names[which(tiss@meta.data$cell_ontology_class == anno)]
# subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)subtiss <- NormalizeData(object = subtiss)Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))[1] "Regressing out nReads" "Regressing out percent.ribo"
[3] "Regressing out Rn45s"
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[1] "Scaling data matrix"
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|================================================================================| 100%subtiss <- FindVariableGenes(object = subtiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.8)Calculating gene means
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|
subtiss <- RunPCA(object = subtiss, pcs.compute = 20)[1] "PC1"
[1] "Postn" "Vtn" "Col6a1" "Pla1a" "Lrp1" "Col6a2" "Slc43a3"
[8] "Col3a1" "Smpdl3a" "Fabp5" "Cfh" "Kdelr3" "Slc12a2" "Plat"
[15] "Lgals3bp" "Nr1h3" "P2ry14" "Tppp3" "Hsd11b1" "Fn1" "Tbx20"
[22] "Spon1" "Lamb1" "Gpr182" "Trib2" "Fbn1" "Chn1" "Asah1"
[29] "Dusp6" "Hic1"
[1] ""
[1] "Tagln" "Myh11" "Nrip2" "Sorbs2" "Acta2"
[6] "Mustn1" "Palld" "Lmod1" "Rcan2" "Pln"
[11] "Wtip" "Csrp1" "Pcp4l1" "Asb2" "Rasd1"
[16] "Myl9" "Sorbs1" "Perp" "Ccdc3" "B230120H23Rik"
[21] "Tpm1" "Clu" "Tpm2" "Gpr20" "Mylk"
[26] "Ptrf" "Ckb" "Cav1" "Npy1r" "Dstn"
[1] ""
[1] ""
[1] "PC2"
[1] "Col14a1" "Loxl1" "Apoe" "Fhl1" "Col6a2" "Rgs2" "Col6a1"
[8] "Eln" "Dkk3" "Fam124a" "Ptgis" "Id2" "Gsn" "Dpt"
[15] "Epdr1" "F2r" "Cdc42ep3" "Igfbp3" "Tmem176b" "Adamts2" "Adh1"
[22] "Srpx" "Hsd11b1" "Cyp1b1" "Lmcd1" "Igfbp4" "Fn1" "Col8a1"
[29] "Stc1" "Col3a1"
[1] ""
[1] "Cxcl12" "Vtn" "Plxdc1" "Ldhb" "P2ry14"
[6] "Plxdc2" "Pcp4l1" "Ddit4l" "Opcml" "Klhl23"
[11] "Eftud2" "Chd2" "Phf17" "Ppfibp2" "Igfbp5"
[16] "Gm20199" "Slc24a3" "Pde4c" "Car2" "Sema7a"
[21] "Sdr42e1" "Osgepl1" "Gm17066" "Htr7" "Ednrb"
[26] "Bet1l" "8430406I07Rik" "Astn1" "Atg16l1" "2410015M20Rik"
[1] ""
[1] ""
[1] "PC3"
[1] "Gpbp1l1" "Dnajb5" "Gm17066" "D930016D06Rik" "Tgfbrap1"
[6] "1810014F10Rik" "Samd14" "Med30" "R74862" "Cwc25"
[11] "Trmt5" "Mocs1" "1700013N18Rik" "Gm20199" "Usp33"
[16] "Pcgf3" "Ctdsp2" "Amotl2" "Rufy3" "Gemin5"
[21] "9930104L06Rik" "Vps13a" "Slc11a2" "Tgfbr1" "Chac2"
[26] "AI480653" "Vezt" "Nkd2" "Zfp408" "Ints7"
[1] ""
[1] "Cd34" "Ifit1" "Plat" "Ifi47" "Ier2"
[6] "Sh2d3c" "Dusp6" "Tgtp2" "Armc10" "Ly6c1"
[11] "Gpihbp1" "Ifit3" "D0H4S114" "Snip1" "Cd274"
[16] "8430408G22Rik" "Ly6a" "Plxdc2" "Eif3d" "E4f1"
[21] "Cmpk2" "Igtp" "Bcar3" "Iigp1" "Tyms"
[26] "P2ry14" "Nudt16" "Mocos" "Btbd19" "1500002O20Rik"
[1] ""
[1] ""
[1] "PC4"
[1] "S1pr3" "Git1" "Fbn1" "Khdrbs3" "Nt5dc1"
[6] "Mknk2" "Fam168a" "Lztfl1" "Xirp1" "Mt1"
[11] "Cecr5" "Spg7" "Ptdss2" "Lgals3bp" "Lars2"
[16] "Gpam" "Ubiad1" "Adnp" "Fkbp14" "Ppp2r2d"
[21] "Magi2" "Zfp276" "Mtor" "Tubgcp4" "Casp7"
[26] "BC068281" "Fyco1" "9130011E15Rik" "Haus2" "Tmem170b"
[1] ""
[1] "Cdc42ep3" "Gpx3" "Fbln5" "Apoe" "Sdc1" "Cd36" "Gstm1"
[8] "Cxcl12" "Chrdl1" "Lims2" "Prelp" "Prss23" "Lmcd1" "Adam15"
[15] "Mafk" "Enpp2" "Eya2" "Lrp4" "Mfap5" "Tbxa2r" "Antxr1"
[22] "Phactr1" "Tppp3" "Ogn" "Tmem119" "Fibin" "Myl9" "Pdgfd"
[29] "Fhl1" "Angptl4"
[1] ""
[1] ""
[1] "PC5"
[1] "Chac2" "Tgfbr1" "Gm17066" "Rufy3" "D930016D06Rik"
[6] "Tgtp2" "Zfp1" "Sc5d" "Tgfbrap1" "Twistnb"
[11] "Znrd1" "Zfp710" "Bcl2l2" "Gm20199" "Nrd1"
[16] "Iigp1" "Psd3" "Slc25a40" "Pcgf3" "1810014F10Rik"
[21] "1700013N18Rik" "Vezt" "Ppp6r3" "Cwc25" "Trmt5"
[26] "Zfp408" "Usp5" "Ifit1" "Zfx" "Gpihbp1"
[1] ""
[1] "Nme6" "Man2b2" "Nrxn1" "Aard" "Arrdc4"
[6] "Zfp119b" "Zfp647" "Nol8" "Rpap3" "Osbpl3"
[11] "Ncoa2" "Ngfr" "Ocrl" "Hipk2" "Slc30a4"
[16] "Kcnt2" "Ephx3" "Eif4enif1" "Dnajc21" "Nr1h4"
[21] "Phxr4" "Rxra" "Cerk" "A230046K03Rik" "Sfxn4"
[26] "Tinf2" "Arfip2" "Hiatl1" "Apba3" "Gon4l"
[1] ""
[1] ""subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)Run Principal Component Analysis.
subtiss <- RunPCA(object = subtiss, do.print = FALSE)
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 250, do.balanced = TRUE, label.columns = FALSE, num.genes = 12)
Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = subtiss)
Choose the number of principal components to use.
# Set number of principal components.
sub.n.pcs = 5The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
# Set resolution
sub.res.used <- 1
subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:sub.n.pcs,
resolution = sub.res.used, print.output = 0, save.SNN = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss <- RunTSNE(object = subtiss, dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=25)# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)
subtiss.markers <- FindAllMarkers(object = subtiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
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|+++ | 5 % ~03s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 06ssubtiss.markers %>% group_by(cluster) %>% top_n(6, avg_diff)Check expression of genes of interset.
genes_to_check = c('Kcnj8','Postn','Myh11','Cnn1')
FeaturePlot(subtiss, genes_to_check, pt.size = 1)
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(subtiss, genes_to_check, plot.legend = T)
How big are the clusters?
table(subtiss@ident)
0 1 2
106 71 44 Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = subtiss, do.return = TRUE, group.by = "plate.barcode")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(subtiss@ident), as.character(subtiss@meta.data$plate.barcode))
B000412 B000633 B000634 B000636 B002010 B002011 B002421 B002423 B002427 B002428
0 3 1 3 2 0 0 6 6 4 3
1 2 7 0 0 1 0 0 2 1 0
2 1 2 1 0 4 2 3 1 4 3
B002429 MAA000398 MAA000399 MAA000400 MAA000452 MAA000586 MAA000587 MAA000589
0 5 5 11 9 12 0 9 4
1 1 9 0 2 1 7 1 2
2 1 3 2 0 1 1 2 5
MAA000594 MAA000899 MAA000901 MAA000903 MAA000906 MAA000917 MAA000918 MAA000919
0 0 0 5 1 0 1 4 4
1 1 1 1 11 1 1 2 1
2 1 0 0 2 0 1 2 1
MAA000920 MAA000936 MAA100037
0 8 0 0
1 0 16 0
2 0 0 1Assigning subcell_ontology_classs
For the subsets, we produce subcell_ontology_classs. These will be written back as metadata in the original object, so we can see all subcell_ontology_classs together.
If some of the clusters you find in the subset deserve additional cell_ontology_class, you can add that right here. Use NA for clusters for which no subcell_ontology_class is needed.
subcluster.ids <- c(0, 1, 2)
subfree_annotation <- c()
subcell_ontology_class <-c("Myofibroblast","Myofibroblast", "Smooth_Muscle_Cells")
subtiss@meta.data[,'subcell_ontology_class'] <- plyr::mapvalues(x = subtiss@ident, from = subcluster.ids, to = subcell_ontology_class)
tiss@meta.data[subtiss@cell.names,'subcell_ontology_class'] <- as.character(subtiss@meta.data$subcell_ontology_class)
TSNEPlot(object = subtiss, do.label = TRUE, pt.size = 0.5, group.by='subcell_ontology_class')
Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/guangli/Documents/GitHub/tabula-muris/00_data_ingest/04_tissue_robj_generated/facsHeart_seurat_tiss.Robj"save(tiss, file=filename)Export the final metadata
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
---
title: "Heart FACS Notebook"
output: html_notebook
---

Specify the tissue of interest, run the boilerplate code which sets up the functions and environment, load the tissue object.

```{r}
tissue_of_interest = "Heart"
library(here)
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
load_tissue_facs(tissue_of_interest)
```

Visualize top genes in principal components

```{r, echo=FALSE, fig.height=4, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 10
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.5

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T)
```

Check expression of genes of interset.

```{r, echo=FALSE, fig.height=12, fig.width=8}

#CM lineage genes
#genes_to_check = c('Sln', 'Myl7','Myl2', 'Tnni3')
#FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3)

#EDC lineage genes
genes_to_check = c('Cdh5', 'Pecam1','Tie1')
FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3) 

FeaturePlot(tiss, features.plot=c('Cdh5', 'Pecam1','Tie1'), cols.use=c("grey","blue"),reduction.use="tsne")

#Fb lineage genes
#genes_to_check = c('Dcn','Gsn','Col1a1', 'Tcf21')
#FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3) 

#SMC genes, blood/immune cell, conduction cells genes
#genes_to_check = c('Col3a1','Cav1','Dcn','Sox9','C1qb','Fabp4','Myh11', 'Vwf', 'Tnni3','Cd37','Tie1','Hbb-b1')
#FeaturePlot(tiss, genes_to_check, pt.size = 1, nCol = 3)

```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=8}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(tiss@ident)
```

Which markers identify a specific cluster?

```{r}
clust.markers <- FindMarkers(object = tiss, ident.1 = 0, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
print(x = head(x= clust.markers, n = 10))
```

You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

Display the top markers you computed above.
```{r}
#tiss.markers %>% group_by(cluster) %>% top_n(5, avg_diff)
```

## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11)
cell_ontology_class <-
  c(
  "fibroblast",
  "endothelial cell",
  "fibroblast",
  "fibroblast",
  "leukocyte",
  "endothelial cell",
  "smooth muscle cell",
  "endocardial cell",
  "cardiac muscle cell",
  "leukocyte",
  "endothelial cell",  
  "erythrocyte"
  )
cell_ontology_id <-
  c(
  "CL:0000057",
  "CL:0000115",
  "CL:0000057",
  "CL:0000057",
  "CL:0000738",
  "CL:0000115",
  "CL:0000192",
  "CL:0002350",
  "CL:0000746",
  "CL:0000738",
  "CL:0000115",
  "CL:0000232"
  )

tiss@meta.data[,'free_annotation'] <- NA
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)

tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)

TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "plate.barcode")
```
###Fb_3 all seem to be derived from mouse_38_F.
##TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.id")

###Edc all are derived from aorta.
##TSNEPlot(object = tiss, do.return = TRUE, group.by = "subtissue")


Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
```



# Subset and iterate

We can repeat the above analysis on a subset of cells, defined using cluster IDs or some other metadata. This is a good way to drill down and find substructure.

## First subset

```{r}
# Subset data based on cluster id
subtiss <- SubsetData(object = tiss, ident.use = c(6), do.center = F, do.scale = F, cells.use = )

# To subset data based on cell_ontology_class or other metadata, you can explicitly pass cell names

# anno = 'exocrine cells'
# cells.to.use = tiss@cell.names[which(tiss@meta.data$cell_ontology_class == anno)]
# subtiss <- SubsetData(object = tiss, cells.use = cells.to.use, do.center = F, do.scale = F)

```

```{r}
subtiss <- NormalizeData(object = subtiss)
subtiss <- ScaleData(object = subtiss, vars.to.regress = c("nReads", "percent.ribo","Rn45s"))
```

```{r}
subtiss <- FindVariableGenes(object = subtiss, do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.8)
subtiss <- RunPCA(object = subtiss, pcs.compute = 20)
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)
```


Run Principal Component Analysis.
```{r}
subtiss <- RunPCA(object = subtiss, do.print = FALSE)
subtiss <- ProjectPCA(object = subtiss, do.print = FALSE)
```

```{r}
# If this fails for your subset, it may be that cells.use is more cells than you have left! Try reducing it.
PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 250, do.balanced = TRUE, label.columns = FALSE, num.genes = 12)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = subtiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
sub.n.pcs = 5
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

```{r}
# Set resolution 
sub.res.used <- 1

subtiss <- FindClusters(object = subtiss, reduction.type = "pca", dims.use = 1:sub.n.pcs, 
    resolution = sub.res.used, print.output = 0, save.SNN = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
subtiss <- RunTSNE(object = subtiss, dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=25)
```

```{r}
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = subtiss, do.label = T)
```

```{r}
subtiss.markers <- FindAllMarkers(object = subtiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```

```{r}
subtiss.markers %>% group_by(cluster) %>% top_n(6, avg_diff)
```

Check expression of genes of interset.
```{r}
genes_to_check = c('Kcnj8','Postn','Myh11','Cnn1')

FeaturePlot(subtiss, genes_to_check, pt.size = 1)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(subtiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(subtiss@ident)
```

## Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = subtiss, do.return = TRUE, group.by = "plate.barcode")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(subtiss@ident), as.character(subtiss@meta.data$plate.barcode))
```



### Assigning subcell_ontology_classs

For the subsets, we produce subcell_ontology_classs. These will be written back as metadata in the original object, so we can see all subcell_ontology_classs together.

If some of the clusters you find in the subset deserve additional cell_ontology_class, you can add that right here. Use NA for clusters for which no subcell_ontology_class is needed.

```{r}
subcluster.ids <- c(0, 1, 2)
subfree_annotation <- c()
subcell_ontology_class <-c("Myofibroblast","Myofibroblast", "Smooth_Muscle_Cells")

subtiss@meta.data[,'subcell_ontology_class'] <- plyr::mapvalues(x = subtiss@ident, from = subcluster.ids, to = subcell_ontology_class)

tiss@meta.data[subtiss@cell.names,'subcell_ontology_class'] <- as.character(subtiss@meta.data$subcell_ontology_class)

TSNEPlot(object = subtiss, do.label = TRUE, pt.size = 0.5, group.by='subcell_ontology_class')
```

# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```


# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_annotation_csv', 
                     paste0(tissue_of_interest, "_annotation.csv"))
write.csv(tiss@meta.data[,c('plate.barcode','cell_ontology_class','cell_ontology_id')], file=filename)
```



